Lightweight mixture faults detection method for gasoline engine using on-line trend analysis

Wu, S; Tang, Z; Guo, Z

Lightweight mixture faults detection method for gasoline engine using on-line trend analysis

Wu, S Tang, Z Guo, Z

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Publication Type:: Journal Article
Citation:: International Journal of Automotive Technology, 2017, 18 (3), pp. 365 - 375
Issue Date:: 2017-06-01

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Field	Value	Language
dc.contributor.author	Wu, S	en_US
dc.contributor.author	Tang, Z	en_US
dc.contributor.author	Guo, Z	en_US
dc.date.issued	2017-06-01	en_US
dc.identifier.citation	International Journal of Automotive Technology, 2017, 18 (3), pp. 365 - 375	en_US
dc.identifier.issn	1229-9138	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127015
dc.description.abstract	© 2017, The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg. Mixture faults detection is meaningful for gasoline engines because proper mixture is the basic prerequisite for healthy running of a combustion engine. Among existing methods for faults detection, the data-driven trend analysis technique is widely used due to the simplicity and efficiency in time-domain. The CUSUM (Cumulative Sum Of Errors) algorithm is good at real-time trend extraction, but it’s easy to be costly on the fuel trim signal during the engine in normal working conditions, which will increase battery energy consumption because engine failure is rarely occurs. Hence, the conventional treatment methods of artifacts in the CUSUM algorithm are modified by means of decay function and detection time analysis. The thresholds are tuned according to the characteristics of artifacts instead of residual variability, which leads to better results of trend extraction and less computation. Then, the revised CUSUM algorithm is used for monitoring the mixture abnormal behaviors, and the mixture faults can be detected in real time through analyzing the variation features of fuel trim signal. The lightweight faults detector using the advanced CUSUM algorithm (FD-A-CUSUM) is evaluated on the experimental data collected from a Ford engine. The validation results show that while engine works under normal conditions, the computation of FD-A-CUSUM has decreased by 72.79 % in comparison with the detection method using the original CUSUM algorithm (FD-O-CUSUM), and the false alarm ratio of FD-A-CUSUM is 3.37 %. Futhermore, the detection results of FD-A-CUSUM for two leakage faults have achieved 91.18 % test accuracy.	en_US
dc.relation.ispartof	International Journal of Automotive Technology	en_US
dc.relation.isbasedon	10.1007/s12239-017-0037-1	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Automobile Design & Engineering	en_US
dc.title	Lightweight mixture faults detection method for gasoline engine using on-line trend analysis	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	18	en_US
utslib.for	0902 Automotive Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	18	en_US

Abstract:

© 2017, The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg. Mixture faults detection is meaningful for gasoline engines because proper mixture is the basic prerequisite for healthy running of a combustion engine. Among existing methods for faults detection, the data-driven trend analysis technique is widely used due to the simplicity and efficiency in time-domain. The CUSUM (Cumulative Sum Of Errors) algorithm is good at real-time trend extraction, but it’s easy to be costly on the fuel trim signal during the engine in normal working conditions, which will increase battery energy consumption because engine failure is rarely occurs. Hence, the conventional treatment methods of artifacts in the CUSUM algorithm are modified by means of decay function and detection time analysis. The thresholds are tuned according to the characteristics of artifacts instead of residual variability, which leads to better results of trend extraction and less computation. Then, the revised CUSUM algorithm is used for monitoring the mixture abnormal behaviors, and the mixture faults can be detected in real time through analyzing the variation features of fuel trim signal. The lightweight faults detector using the advanced CUSUM algorithm (FD-A-CUSUM) is evaluated on the experimental data collected from a Ford engine. The validation results show that while engine works under normal conditions, the computation of FD-A-CUSUM has decreased by 72.79 % in comparison with the detection method using the original CUSUM algorithm (FD-O-CUSUM), and the false alarm ratio of FD-A-CUSUM is 3.37 %. Futhermore, the detection results of FD-A-CUSUM for two leakage faults have achieved 91.18 % test accuracy.

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http://hdl.handle.net/10453/127015